A new pairwise kernel for biological network inference with
support vector machines

Jean-Philippe Vert, Jian Qiu and William Stafford Noble

BMC Bioinformatics. 8(Suppl. 10):S8, 2007.

Abstract

BACKGROUND: Much recent work in bioinformatics has focused on the
inference of various types of biological networks, representing gene
regulation, metabolic processes, protein-protein interactions, etc. A
common setting involves inferring network edges in a supervised
fashion from a set of high-confidence edges, possibly characterized by
multiple, heterogeneous data sets (protein sequence, gene expression,
etc.).

RESULTS: Here, we distinguish between two modes of inference in
this setting: direct inference based upon similarities between nodes
joined by an edge, and indirect inference based upon similarities
between one pair of nodes and another pair of nodes. We propose a
supervised approach for the direct case by translating it into a
distance metric learning problem. A relaxation of the resulting convex
optimization problem leads to the support vector machine (SVM)
algorithm with a particular kernel for pairs, which we call the metric
learning pairwise kernel. This new kernel for pairs can easily be used
by most SVM implementations to solve problems of supervised
classification and inference of pairwise relationships from
heterogeneous data. We demonstrate, using several real biological
networks and genomic datasets, that this approach often improves upon
the state-of-the-art SVM for indirect inference with another pairwise
kernel, and that the combination of both kernels always improves upon
each individual kernel.

CONCLUSION: The metric learning pairwise kernel is a new
formulation to infer pairwise relationships with SVM, which provides
state-of-the-art results for the inference of several biological
networks from heterogeneous genomic data.